Similar to QEFVs, in model derived features, the token embedding is often used in quality estimation models [1]}, [2]} to project the hidden representation \(z\)  derived from the predictors for each target token \(y\)  to the desired representations for QE task.
In our model, the hidden representation \(z\)  is the decoder output before the final prediction layer, derived by first encoding the source sentence \(x\)  with a conditional encoder \(EncA\)  and decoding the encoder output \(h_x\)  together with the target \(y\)  with \(EncB\) ,
\(\begin{aligned}h_x &= EncA.encode(x)\\z &= EncB.decode(h_x, y)\end{aligned}\) 
